resample irregularly spaced data in pandas
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Chapters
00:00 Question
02:03 Accepted answer (Score 4)
02:48 Thank you
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Full question
https://stackoverflow.com/questions/4111...
Accepted answer links:
[pd.to_datetime]: http://pandas.pydata.org/pandas-docs/sta...
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Tags
#python #pandas
#avk47
--
Music by Eric Matyas
https://www.soundimage.org
Track title: Luau
--
Chapters
00:00 Question
02:03 Accepted answer (Score 4)
02:48 Thank you
--
Full question
https://stackoverflow.com/questions/4111...
Accepted answer links:
[pd.to_datetime]: http://pandas.pydata.org/pandas-docs/sta...
--
Content licensed under CC BY-SA
https://meta.stackexchange.com/help/lice...
--
Tags
#python #pandas
#avk47
ACCEPTED ANSWER
Score 4
You don't need to explicitly use DatetimeIndex, just set 'time' as the index and pandas will take care of the rest, so long as your 'time' column has been converted to datetime using pd.to_datetime or some other method. Additionally, you don't need to resample each column individually if you're using the same method; just do it on the entire DataFrame.
# Convert to datetime, if necessary.
df['time'] = pd.to_datetime(df['time'])
# Set the index and resample (using month start freq for compact output).
df = df.set_index('time')
df = df.resample('MS').mean()
The resulting output:
fraction number
time
2014-10-01 0.435441 0.5
2014-11-01 0.430544 6.5
2014-12-01 0.627552 13.5